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the output was evaluated against reference translations using bleu score which ranges from 0 to 1---we created 5-gram language models for every domain using srilm with improved kneserney smoothing on the target side of the training parallel corpora | 0 |
the weights associated to feature functions are optimally combined using the minimum error rate training---we set the feature weights by optimizing the bleu score directly using minimum error rate training on the development set | 1 |
the n-gram language models are trained using the srilm toolkit or similar software developed at hut---all language models are created with the srilm toolkit and are standard 4-gram lms with interpolated modified kneser-ney smoothing | 1 |
we trained a 4-gram language model with kneser-ney smoothing and unigram caching using the sri-lm toolkit---we used 5-gram models , estimated using the sri language modeling toolkit with modified kneser-ney smoothing | 1 |
contrast to joint methods , this paper proposes to exploit argument information explicitly for ed---in this work , we propose to exploit argument information explicitly for ed | 1 |
recently , with the development of neural network , deep learning based models attract much attention in various tasks---additionally , a back-off 2-gram model with goodturing discounting and no lexical classes was built from the same training data , using the srilm toolkit | 0 |
the use of word unigrams is a standard approach in text classification , and has also been successfully used to predict reading difficulty---using appropriate word weighting functions is known to improve the performance of text categorization | 1 |
we propose min ( memory interaction network ) , a novel lstm-based deep multi-task learning framework for the ate task---yessenalina and cardie model each word as a matrix and combine words using iterated matrix multiplication | 0 |
twitter 1 is a microblogging service , which according to latest statistics , has 284 million active users , 77 % outside the us that generate 500 million tweets a day in 35 different languages---twitter is a microblogging site where people express themselves and react to content in real-time | 1 |
the hierarchical phrasebased translation model , which adopts a synchronous context-free grammar , is considered to be prominent in capturing global reorderings---we present connectionist bidirectional rnn models which are especially suited for sentence classification tasks | 0 |
latent dirichlet allocation is a generative probabilistic topic model where documents are represented as random mixtures over latent topics , characterized by a distribution over words---latent dirichlet allocation is a popular probabilistic model that learns latent topics from documents and words , by using dirichlet priors to regularize the topic distributions | 1 |
we use the penn wsj treebank for our experiments---as with , we train the language model on the penn treebank | 1 |
the task is to classify whether each sentence provides the answer to the query---qa , the task is to pick sentences that are most relevant to the question | 1 |
we do so because character n-gram based approaches have largely outperformed function word based approaches indicating that lexical words may also help with authorship attribution---however , character n-gram based approaches have largely outperformed function word based approaches indicating that some lexical words may also help with authorship attribution | 1 |
the original pmg implementation has utilised conditional random fields , due to the considerable representation capabilities of this model---we use logistic regression as the per-class binary classifier , implemented using liblinear | 0 |
to the best of our knowledge the connection between the decipherment problem and the quadratic assignment problem was not known---we use srilm toolkit to build a 5-gram language model with modified kneser-ney smoothing | 0 |
in this paper , we present a machine learning approach to the identification and resolution of chinese anaphoric zero pronouns---and our present work is the first to perform both identification and resolution of chinese anaphoric zero pronouns using a machine learning approach | 1 |
a 3-gram language model was trained from the target side of the training data for chinese and arabic , using the srilm toolkit---a 4-gram language model was trained on the target side of the parallel data using the srilm toolkit | 1 |
the results reported here indicate that the proposed methodology yields usable results in understanding the qur ’ an on the basis of its lexical semantics---preliminary results indicate that construction and semantic interpretation of cluster trees based on lexical frequency is a useful approach to discovering thematic interrelationships among the suras that constitute the qur ’ an | 1 |
we use srilm toolkits to train two 4-gram language models on the filtered english blog authorship corpus and the xinhua portion of gigaword corpus , respectively---we used the srilm toolkit to create 5-gram language models with interpolated modified kneser-ney discounting | 1 |
we used the penn treebank to perform empirical experiments on the proposed parsing models---in our previous work , we used the wsj penn treebank to train the mixed trigram model | 1 |
our machine translation system is a phrase-based system using the moses toolkit---we used moses , a phrase-based smt toolkit , for training the translation model | 1 |
collobert et al initially introduced neural networks into the srl task---in our experiments we use a publicly available implementation of conditional random fields | 0 |
to capture the hierarchical relationship among codes , we build a tree lstm along the code tree---in this paper , drawing intuition from the turing test , we propose using adversarial training for open-domain dialogue generation | 0 |
firstly , we built a forward 5-gram language model using the srilm toolkit with modified kneser-ney smoothing---we used a 5-gram language model with modified kneser-ney smoothing implemented using the srilm toolkit | 1 |
syntactic knowledge is important for discourse relation recognition---the feature weights are tuned to optimize bleu using the minimum error rate training algorithm | 0 |
t盲ckstr枚m et al derive crosslingual clusters from bitext to help delexicalized parser transfer---t盲ckstr枚m et al use cross-lingual word clusters to show transfer of linguistic structure | 1 |
we present in detail the framework of the twin-candidate model for anaphora resolution---we have introduced in detail the framework of the twin-candidate model for anaphora resolution | 1 |
importantly , word embeddings have been effectively used for several nlp tasks---unsupervised word embeddings trained from large amounts of unlabeled data have been shown to improve many nlp tasks | 1 |
word sense disambiguation ( wsd ) is a natural language processing ( nlp ) task in which the correct meaning ( sense ) of a word in a given context is to be determined---in natural language , a word often assumes different meanings , and the task of determining the correct meaning , or sense , of a word in different contexts is known as word sense disambiguation ( wsd ) | 1 |
the target fourgram language model was built with the english part of training data using the sri language modeling toolkit---with shared parameters , the model is able to learn a general way to act in slots , increasing its scalability to large domains | 0 |
these predictions-as-features style methods model high order label dependencies and obtain high performance---multi-faceted approach , motivated by the diversity of input data imperfections , can eliminate a large proportion of the spurious outputs | 0 |
we used the srilm toolkit to create 5-gram language models with interpolated modified kneser-ney discounting---we use srilm train a 5-gram language model on the xinhua portion of the english gigaword corpus 5th edition with modified kneser-ney discounting | 1 |
the network was trained using stochastic gradient descent with adam---all neural networks were trained using adam optimizer | 1 |
bleu is a precision measure based on m-gram count vectors---bleu is used as a standard evaluation metric | 1 |
in this paper , we presented a self-attentive hybrid gru-based network for predicting valence intensity for short text---in this paper , we present a self-attentive hybrid gru-based network ( sahgn ) that competed at semeval-2018 task | 1 |
the phrase-based model segments a bilingual sentence pair into phrases that are continuous sequences of words---phrase-based smt segments a bilingual sentence pair into phrases that are continuous sequences of words or discontinuous sequences of words | 1 |
early approaches to mwes identification concentrated on their collocational behavior---early approaches to identifying mwes concentrated on their collocational behavior | 1 |
our training data is the switchboard portion of the english penn treebank corpus , which consists of telephone conversations about assigned topics---we describe an alternative , a latent variable model , to learn long range dependencies | 0 |
we used the first-stage parser of charniak and johnson for english and bitpar for german---we primarily used the charniak-johnson generative parser to parse the english europarl data and the test data | 1 |
latent dirichlet allocation is one of the widely adopted generative models for topic modeling---latent dirichlet allocation is one of the most popular topic models used to mine large text data sets | 1 |
recurrent neural networks have successfully been used in sequence learning problems , for example machine translation , and language modeling---neural networks have been successfully applied to nlp problems , specifically , sequence-to-sequence or models applied to machine translation and word-to-vector | 1 |
the weights of the different feature functions were optimised by means of minimum error rate training on the 2013 wmt test set---the weights of the log-linear interpolation were optimized by means of mert , using the news-commentary test set of the 2008 shared task as a development set | 1 |
all linear models were trained with the perceptron update rule---these models were implemented using the package scikit-learn | 1 |
as such , masc is the first large-scale , open , community-based effort to create much needed language resources for nlp---as such , masc is the first large-scale , open , community-based effort to create a much-needed language resource for nlp | 1 |
we use stanford log-linear partof-speech tagger to produce pos tags for the english side---we use the stanford nlp pos tagger to generate the tagged text | 1 |
in this paper , we present gated self-matching networks for reading comprehension and question answering---we propose a self-matching attention mechanism to refine the representation | 1 |
we also use a 4-gram language model trained using srilm with kneser-ney smoothing---we build a 9-gram lm using srilm toolkit with modified kneser-ney smoothing | 1 |
moreover , mmrbased feature selection sometimes produces some improvements of conventional machine learning algorithms over svm which is known to give the best classification accuracy---following foulds et al , we perform simulated annealing which varies the m-h acceptance ratio to improve mixing | 0 |
in this paper we focus on a new problem of event coreference resolution across television news videos---coreference resolution is a set partitioning problem in which each resulting partition refers to an entity | 0 |
for language model , we used sri language modeling toolkit to train a 4-gram model with modified kneser-ney smoothing---we use sri language modeling toolkit to train a 5-gram language model on the english sentences of fbis corpus | 1 |
the language models were trained using srilm toolkit---the target-side language models were estimated using the srilm toolkit | 1 |
the experimental results and analysis demonstrate that our model is effective in exploiting both source and target document context , and statistically significantly outperforms the previous work in terms of bleu and meteor---documents show that our model is effective in exploiting both source and target document context , and statistically significantly outperforms the previous work in terms of bleu and meteor | 1 |
recently there are some efforts in applying machine learning approaches to the acquisition of dialogue strategies---machine learning techniques , and particularly reinforcement learning , have recently received great interest in research on dialogue management | 1 |
for part of speech tagging and dependency parsing of the text , we used the toolset from stanford corenlp---we preprocess the texts using the stanford corenlp suite for tokenization , lemmatization , part-of-speech tagging , and named entity recognition | 1 |
lda is a simple model for topic modeling where topic probabilities are assigned words in documents---in this paper , we outline an approach to detecting such egregious conversations , using behavioral cues | 0 |
named entity recognition ( ner ) is the task of identifying and typing phrases that contain the names of persons , organizations , locations , and so on---named entity recognition ( ner ) is a fundamental information extraction task that automatically detects named entities in text and classifies them into predefined entity types such as person , organization , gpe ( geopolitical entities ) , event , location , time , date , etc | 1 |
the experimental results demonstrate that our approach outperforms the template extraction based approaches---the latter approach represents word contexts as vectors in some space and uses similarity measures and automatic clustering in that space | 0 |
liu et al allow for application of nonsyntactic phrase pairs in their tree-to-string alignment template system---liu et al also add non-syntactic pbsmt phrases into their tree-to-string translation system | 1 |
semeval is the international workshop on semantic evaluation that has evolved from senseval---flat tags can be relaxed , using context , with the resulting polysemous clustering outperforming gold part-of-speech tags for the english dependency grammar induction task | 0 |
finally , our work is similar to the comparison of the chart-based mstparser and shift-reduce maltparser for dependency parsing---the comparison reported in this section is similar to the comparison between the chartbased mstparser and shiftreduce maltparser for dependency parsing | 1 |
for the compilation , we focus on travel blogs , which are defined as travel journals written by bloggers in diary form---for the compilation , we focused on travel blogs , which are defined as travel journals | 1 |
we also present a novel baseline that performs remarkably well without using topic identification---here , we present an approach that instead uses distant supervision | 1 |
in this sense , our work follows foster et al , who weigh out-of-domain phrase pairs according to their relevance to the target domain---in this sense , our work follows foster , goutte , and kuhn , who weigh out-of-domain phrase pairs according to their relevance to the target domain | 1 |
we trained word embeddings for this dataset using word2vec on over around 10m documents of clinical records---we used the pre-trained word embeddings that are learned using the word2vec toolkit on google news dataset | 1 |
coreference resolution is the task of determining when two textual mentions name the same individual---since coreference resolution is a pervasive discourse phenomenon causing performance impediments in current ie systems , we considered a corpus of aligned english and romanian texts to identify coreferring expressions | 1 |
it has been shown that user opinions about products , companies and politics can be influenced by opinions posted by other online users in online forums and social networks---it has been shown that user opinions about products , companies and politics can be influenced by opinions posted by other online users | 1 |
this paper describes a system that allows users to explore large cultural heritage collections---this paper describes a system for navigating large collections of information about cultural heritage | 1 |
our approach is similar to conneau et al where authors investigate transfer learning to find universal sentence representation---as erhan et al reported , word embeddings learned from a significant amount of unlabeled data are more powerful for capturing the meaningful semantic regularities of words | 0 |
the n-gram language models are trained using the srilm toolkit or similar software developed at hut---the pre-processed monolingual sentences will be used by srilm or berkeleylm to train a n-gram language model | 1 |
we use srilm for training a trigram language model on the english side of the training corpus---with the proposed discriminative model , we can directly optimize the search phase of query spelling correction | 0 |
openccg uses a hybrid symbolic-statistical chart realizer which takes logical forms as input and produces sentences by using ccg combinators to combine signs---we used glove 10 to learn 300-dimensional word embeddings | 0 |
informally , nlg is the production of a natural language text from computer-internal representation of information , where nlg can be seen as a complex -- potentially cascaded -- decision making process---nlg is a critical component in a dialogue system , where its goal is to generate the natural language given the semantics provided by the dialogue manager | 1 |
for language modeling , we use the english gigaword corpus with 5-gram lm implemented with the kenlm toolkit---the un-pre-marked japanese corpus is used to train a language model using kenlm | 1 |
we use the word2vec vectors with 300 dimensions , pre-trained on 100 billion words of google news---to encode the original sentences we used word2vec embeddings pre-trained on google news | 1 |
the types of events to extract are known in advance---we use pre-trained glove vector for initialization of word embeddings | 0 |
mert was used to tune development set parameter weights and bleu was used on test sets to evaluate the translation performance---the mert was used to tune the feature weights on the development set and the translation performance was evaluated on the test set with the tuned weights | 1 |
however , no single approach alone can cover the entire smart selection problem---none alone solves the entire smart selection problem | 1 |
the visargue framework provides a novel visual analytics toolbox for exploratory and confirmatory analyses of multi-party discourse data---we present a novel visual analytics framework that encodes various layers of discourse properties and allows for an analysis of multi-party discourse | 1 |
to gather examples from these parallel corpora , we followed the approach in---to gather examples from parallel corpora , we followed the approach in | 1 |
translation results are evaluated using the word-based bleu score---koo et al used the brown algorithm to learn word clusters from a large amount of unannotated data and defined a set of word cluster-based features for dependency parsing models | 0 |
in addition , the concept of iteratmath-w-5-3-1-66ant to planning , so that a generalisation across distributives and iteratives plus what has been said about their temporal nature should have interesting implications in this area---and since the concept of iterated action is central to planning , the generalisation across iteration and distributives , along with the observations about their nature , have interesting implications for work in this area | 1 |
we evaluated translation quality using uncased bleu and ter---the translation outputs were evaluated with bleu and meteor | 1 |
coreference resolution is a task aimed at identifying phrases ( mentions ) referring to the same entity---coreference resolution is a set partitioning problem in which each resulting partition refers to an entity | 1 |
we represent each word as a vector using twitter glove embedding---we represent terms using pre-trained glove wikipedia 6b word embeddings | 1 |
the outline of this paper is as follows : in section 2 , we review current approaches to building dialog systems---in this paper , we discuss methods for automatically creating models of dialog structure | 1 |
the main goal of this paper is to propose automatic schemes for the translation paired comparison method---in this paper , we propose an automatization scheme for the translation paired comparison method that employs available automatic | 1 |
we created 5-gram language models for every domain using srilm with improved kneserney smoothing on the target side of the training parallel corpora---using multi-layered neural networks to learn word embeddings has become standard in nlp | 0 |
the discussed model in this contribution is an extension of the classical top-down tree transducer , which was introduced by rounds and thatcher---the model discussed in this contribution is an extension of the classical top-down tree transducer , which was introduced by rounds and thatcher | 1 |
collobert et al used a large amount of unlabeled data to map words to high-dimensional vectors and a neural network architecture to generate an internal representation---in this work , we uncover several latent semantic structures behind humor , in terms of meaning | 0 |
for samt grammar extraction , we parsed the english training data using the berkeley parser with the provided treebank-trained grammar---to extract the features of the rule selection model , we parse the english part of our training data using the berkeley parser | 1 |
we train a kn-smoothed 5-gram language model on the target side of the parallel training data with srilm---we use srilm toolkit to train a trigram language model with modified kneser-ney smoothing on the target side of training corpus | 1 |
the phrasebased machine translation uses the grow-diag-final heuristic to extend the word alignment to phrase alignment by using the intersection result---the international corpus of learner english was widely used until recently , despite its shortcomings 1 being widely noted | 0 |
in this paper we propose a novel approach to identify asymmetric relations between verbs---in this paper we presented a method to discover asymmetric entailment relations between verbs | 1 |
to provide a standard benchmark for english sts , we present the sts benchmark , a careful selection of the english data sets from previous sts tasks ( 2012-2017 )---for assessing new methods , we present the sts benchmark , a publicly available selection of data from english sts | 1 |
in recent years , phrase-based systems for statistical machine translation have delivered state-of-the-art performance on standard translation tasks---during the last four years , various implementations and extentions to phrase-based statistical models have led to significant increases in machine translation accuracy | 1 |
we also conduct human evaluations on abstractive summarization and find that our method outperforms a purely supervised learning baseline in a statistically significant manner---the translations are evaluated in terms of bleu score | 0 |
brown , et al describe a statistical algorithm for partitioning word senses into two groups---we ran mt experiments using the moses phrase-based translation system | 0 |
we use minimum error rate training to tune the feature weights of hpb for maximum bleu score on the development set with serval groups of different start weights---in practice , policy gradient method is usually used to calculate gradients for the generator due to discrete symbols | 0 |
in this paper we propose a method for defining kernels in terms of a probabilistic model of parsing---pseudo-word is a kind of multi-word expression ( includes both unary word and multi-word ) | 0 |
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